Abstract
In this paper, an investigation into the performance of multilayered radial basis functions (RBF) networks is conducted which use Gaussian function in place of sigmoidal function in multilayered neural networks (NNs). The focus is on the difference of approximation abilities between multilayered RBF networks and NNs. A function approximation problem is employed to evaluate the performance of multilayered RBF networks, and several types of different functions are used as the functions to be approximated. Gradient method is employed to optimize the parameters including centers, widths, and linear connection weights to the output nodes. It is shown from the result that RBF does not always have significant advantages over sigmoidal functions when they are used in multilayered networks.
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